OpenRaiser/Intern-Atlas huggingface dataset:
Subcarrier-Chunk Assignment With Power Allocation and Multiple-Rate Constraints for Downlink OFDMA
Venue: arXiv cs.IT 2017
Year: 2017

We consider downlink OFDMA in which users are imposed with different proportional-rate constraints, and a base station would like to maximise user rate over subcarrier assignment (SA) and power allocation (PA). To attain the objective, we propose a chunk-based SA and low-complexity PA algorithms. Numerical results show that the proposed SA and PA can provide higher average minimum achievable rate than the existing schemes while keeping the deviation from the rate constraint small for both single-cell and multi-cell systems with fractional frequency reuse.
Evaluation of Hashing Methods Performance on Binary Feature Descriptors
Venue: arXiv cs.CV 2017
Year: 2017

In this paper we evaluate performance of data-dependent hashing methods on binary data. The goal is to find a hashing method that can effectively produce lower dimensional binary representation of 512-bit FREAK descriptors. A representative sample of recent unsupervised, semi-supervised and supervised hashing methods was experimentally evaluated on large datasets of labelled binary FREAK feature descriptors.
Hierarchical Partial Planarity
Venue: arXiv cs.DS 2017
Year: 2017

In this paper we consider graphs whose edges are associated with a degree of {\em importance}, which may depend on the type of connections they represent or on how recently they appeared in the scene, in a streaming setting. The goal is to construct layouts of these graphs in which the readability of an edge is proportional to its importance, that is, more important edges have fewer crossings. We formalize this problem and study the case in which there exist three different degrees of importance. We give a polynomial-time testing algorithm when the graph induced by the two most important sets of edges is biconnected. We also discuss interesting relationships with other constrained-planarity problems.
A non-commutative algorithm for multiplying 5x5 matrices using 99 multiplications
Venue: arXiv cs.CC 2017
Year: 2017

We present a non-commutative algorithm for multiplying 5x5 matrices using 99 multiplications. This algorithm is a minor modification of Makarov's algorithm which exhibit the previous best known bound with 100 multiplications.
Fog Radio Access Networks: Mobility Management, Interference Mitigation and Resource Optimization
Venue: arXiv cs.IT 2017
Year: 2017

In order to make Internet connections ubiquitous and autonomous in our daily lives, maximizing the utilization of radio resources and social information is one of the major research topics in future mobile communication technologies. Fog radio access network (FRAN) is regarded as a promising paradigm for the fifth generation (5G) of mobile networks. FRAN integrates fog computing with RAN and makes full use of the edge of networks. FRAN would be different in networking, computing, storage and control as compared with conventional radio access networks (RAN) and the emerging cloud RAN. In this article, we provide a description of the FRAN architecture, and discuss how the distinctive characteristics of FRAN make it possible to efficiently alleviate the burden on the fronthaul, backhaul and backbone networks, as well as reduce content delivery latencies. We will focus on the mobility management, interference mitigation, and resource optimization in FRAN. Our simulation results show that the proposed FRAN architecture and the associated mobility and resource management mechanisms can reduce the signaling cost and increase the net utility for the RAN.
Community Networks and Sustainability: a Survey of Perceptions, Practices, and Proposed Solutions
Venue: arXiv cs.NI 2017
Year: 2017

Community network (CN) initiatives have been around for roughly two decades, evangelizing a distinctly different paradigm for building, maintaining, and sharing network infrastructure but also defending the basic human right to Internet access. Over this time they have evolved into a mosaic of systems that vary widely with respect to their network technologies, their offered services, their organizational structure, and the way they position themselves in the overall telecommunications' ecosystem. Common to all these highly differentiated initiatives is the sustainability challenge. We approach sustainability as a broad term with an economical, political, and cultural context. We first review the different perceptions of the term. These vary both across and within the different types of stakeholders involved in CNs and are reflected in their motivation to join such initiatives. Then, we study the diverse ways that CN operators pursue the sustainability goal. Depending on the actual context of the term, these range all the way from mechanisms to fund their activities and synergistic approaches with commercial service providers, to organizational structures and social activities that serve as incentives to maximize the engagement of their members. Finally, we iterate and discuss theoretical concepts of incentive mechanisms that have been proposed in the literature for these networks as well as implemented tools and processes designed to set the ground for CN participation. While, theoretical mechanisms leverage game theory, reputation frameworks, and social mechanisms, implemented mechanisms focus on organizational matters, education and services, all aiming to motivate the active and sustained participation of users and other actors in the CN.
Securing Visible Light Communication Systems by Beamforming in the Presence of Randomly Distributed Eavesdroppers
Venue: arXiv cs.IT 2017
Year: 2017

This paper considers secrecy enhancement mechanisms in visible light communication (VLC) systems with spatially distributed passive eavesdroppers (EDs) under the assumption that there are multiple LED transmitters and one legitimate receiver (UE). Based on certain amplitude constraints, we propose an optimal beamforming scheme to optimize secrecy performance. Contrary to the case where null-steering is made possible by using knowledge of the ED locations, we show that the optimal solution when only statistical information about ED locations is available directs the transmission along a particular eigenmode related to the intensity of the ED process and the intended channel. Then, a sub-optimal LED selection scheme is provided to reduce the secrecy outage probability (SOP). An approximate closed-form for the SOP is derived by using secrecy capacity bounds. All analysis is numerically verified by Monte Carlo simulations. The analysis shows that the optimal beamformer yields superior performance to LED selection. However, LED selection is still a highly efficient suboptimal scheme due to the complexity associated with the use of multiple transmitters in the full beamforming approach. These performance trends and exact relations between system parameters can be used to develop a secure VLC system in the presence of randomly distributed EDs.
Reconstruction of Word Embeddings from Sub-Word Parameters
Venue: arXiv cs.CL 2017
Year: 2017

Pre-trained word embeddings improve the performance of a neural model at the cost of increasing the model size. We propose to benefit from this resource without paying the cost by operating strictly at the sub-lexical level. Our approach is quite simple: before task-specific training, we first optimize sub-word parameters to reconstruct pre-trained word embeddings using various distance measures. We report interesting results on a variety of tasks: word similarity, word analogy, and part-of-speech tagging.
Extracting and Exploiting Inherent Sparsity for Efficient IoT Support in 5G: Challenges and Potential Solutions
Venue: arXiv cs.NI 2017
Year: 2017

Besides enabling an enhanced mobile broadband, next generation of mobile networks (5G) are envisioned for the support of massive connectivity of heterogeneous Internet of Things (IoT)s. These IoTs are envisioned for a large number of use-cases including smart cities, environment monitoring, smart vehicles, etc. Unfortunately, most IoTs have very limited computing and storage capabilities and need cloud services. Hence, connecting these devices through 5G systems requires huge spectrum resources in addition to handling the massive connectivity and improved security. This article discusses the challenges facing the support of IoTs through 5G systems. The focus is devoted to discussing physical layer limitations in terms of spectrum resources and radio access channel connectivity. We show how sparsity can be exploited for addressing these challenges especially in terms of enabling wideband spectrum management and handling the connectivity by exploiting device-to-device communications and edge-cloud. Moreover, we identify major open problems and research directions that need to be explored towards enabling the support of massive heterogeneous IoTs through 5G systems.
Adaptive Quorum-based Channel-hopping Distributed Coordination Scheme for Cognitive Radio Networks
Venue: arXiv cs.NI 2017
Year: 2017

In this paper, we propose novel channel-hoppingbased distributed rendezvous algorithm based on grid-basedquorum techniques. The proposed algorithm increases the probability of rendezvous (RDV) within a single cycle by allowing CR nodes to meet more often according to intersection property of quorum systems (QSs). Our proposed algorithm is called Adaptive quorum-based channel-hopping distributed coordination scheme for cognitive radio networks. The main idea of our algorithm is to dynamically adjust the selected QS by CR users according to the varying traffic loads in the CRN. The proposed algorithm decreases the average time to rendezvous (TTR) and increase the probability of RDV. We evaluate the performance of our algorithm through simulations. The performance of our algorithm is compared with two different schemes. The results show that our algorithm can reduce TTR, increase the RDV, and decrease the energy consumption per successful RDV.
Multipath Multiplexing for Capacity Enhancement in SIMO Wireless Systems
Venue: arXiv cs.IT 2017
Year: 2017

This paper proposes a novel and simple orthogonal faster than Nyquist (OFTN) data transmission and detection approach for a single input multiple output (SIMO) system. It is assumed that the signal having a bandwidth $B$ is transmitted through a wireless channel with $L$ multipath components. Under this assumption, the current paper provides a novel and simple OFTN transmission and symbol-by-symbol detection approach that exploits the multiplexing gain obtained by the multipath characteristic of wideband wireless channels. It is shown that the proposed design can achieve a higher transmission rate than the existing one (i.e., orthogonal frequency division multiplexing (OFDM)). Furthermore, the achievable rate gap between the proposed approach and that of the OFDM increases as the number of receiver antennas increases for a fixed value of $L$. This implies that the performance gain of the proposed approach can be very significant for a large-scale multi-antenna wireless system. The superiority of the proposed approach is shown theoretically and confirmed via numerical simulations. {Specifically, we have found {upper-bound average} rates of 15 bps/Hz and 28 bps/Hz with the OFDM and proposed approaches, respectively, in a Rayleigh fading channel with 32 receive antennas and signal to noise ratio (SNR) of 15.3 dB. The extension of the proposed approach for different system setups and associated research problems is also discussed.
Adaptive Channel Prediction, Beamforming and Scheduling Design for 5G V2I Network
Venue: arXiv cs.IT 2017
Year: 2017

One of the important use-cases of 5G network is the vehicle to infrastructure (V2I) communication which requires accurate understanding about its dynamic propagation environment. As 5G base stations (BSs) tend to have multiple antennas, they will likely employ beamforming to steer their radiation pattern to the desired vehicle equipment (VE). Furthermore, since most wireless standards employ an OFDM system, each VE may use one or more sub-carriers. To this end, this paper proposes a joint design of adaptive channel prediction, beamforming and scheduling for 5G V2I communications. The channel prediction algorithm is designed without the training signal and channel impulse response (CIR) model. In this regard, first we utilize the well known adaptive recursive least squares (RLS) technique for predicting the next block CIR from the past and current block received signals (a block may have one or more OFDM symbols). Then, we jointly design the beamforming and VE scheduling for each sub-carrier to maximize the uplink channel average sum rate by utilizing the predicted CIR. The beamforming problem is formulated as a Rayleigh quotient optimization where its global optimal solution is guaranteed. And, the VE scheduling design is formulated as an integer programming problem which is solved by employing a greedy search. The superiority of the proposed channel prediction and scheduling algorithms over those of the existing ones is demonstrated via numerical simulations.
Towards Optimality in Parallel Scheduling
Venue: arXiv cs.PF 2017
Year: 2017

To keep pace with Moore's law, chip designers have focused on increasing the number of cores per chip rather than single core performance. In turn, modern jobs are often designed to run on any number of cores. However, to effectively leverage these multi-core chips, one must address the question of how many cores to assign to each job. Given that jobs receive sublinear speedups from additional cores, there is an obvious tradeoff: allocating more cores to an individual job reduces the job's runtime, but in turn decreases the efficiency of the overall system. We ask how the system should schedule jobs across cores so as to minimize the mean response time over a stream of incoming jobs. To answer this question, we develop an analytical model of jobs running on a multi-core machine. We prove that EQUI, a policy which continuously divides cores evenly across jobs, is optimal when all jobs follow a single speedup curve and have exponentially distributed sizes. EQUI requires jobs to change their level of parallelization while they run. Since this is not possible for all workloads, we consider a class of "fixed-width" policies, which choose a single level of parallelization, k, to use for all jobs. We prove that, surprisingly, it is possible to achieve EQUI's performance without requiring jobs to change their levels of parallelization by using the optimal fixed level of parallelization, k*. We also show how to analytically derive the optimal k* as a function of the system load, the speedup curve, and the job size distribution. In the case where jobs may follow different speedup curves, finding a good scheduling policy is even more challenging. We find that policies like EQUI which performed well in the case of a single speedup function now perform poorly. We propose a very simple policy, GREEDY*, which performs near-optimally when compared to the numerically-derived optimal policy.
PatchShuffle Regularization
Venue: arXiv cs.CV 2017
Year: 2017

This paper focuses on regularizing the training of the convolutional neural network (CNN). We propose a new regularization approach named ``PatchShuffle`` that can be adopted in any classification-oriented CNN models. It is easy to implement: in each mini-batch, images or feature maps are randomly chosen to undergo a transformation such that pixels within each local patch are shuffled. Through generating images and feature maps with interior orderless patches, PatchShuffle creates rich local variations, reduces the risk of network overfitting, and can be viewed as a beneficial supplement to various kinds of training regularization techniques, such as weight decay, model ensemble and dropout. Experiments on four representative classification datasets show that PatchShuffle improves the generalization ability of CNN especially when the data is scarce. Moreover, we empirically illustrate that CNN models trained with PatchShuffle are more robust to noise and local changes in an image.
AutOMP: An Automatic OpenMP Parallelization Generator for Variable-Oriented High-Performance Scientific Codes
Venue: arXiv cs.DC 2017
Year: 2017

OpenMP is a cross-platform API that extends C, C++ and Fortran and provides shared-memory parallelism platform for those languages. The use of many cores and HPC technologies for scientific computing has been spread since the 1990s, and now takes part in many fields of research. The relative ease of implementing OpenMP, along with the development of multi-core shared memory processors (such as Intel Xeon Phi) makes OpenMP a favorable method for parallelization in the process of modernizing a legacy codes. Legacy scientific codes are usually holding large number of physical arrays which being used and updated by the code routines. In most of the cases the parallelization of such code focuses on loop parallelization. A key step in this parallelization is deciding which of the variables in the parallelized scope should be private (so each thread will hold a copy of them), and which variables should be shared across the threads. Other important step is finding which variables should be synchronized after the loop execution. In this work we present an automatic pre-processor that preforms these stages - AutOMP (Automatic OpenMP). AutOMP recognize all the variables assignments inside a loop. These variables will be private unless the assignment is of an array element which depend on the loop index variable. Afterwards, AutOMP finds the places where threads synchronization is needed, and which reduction operator is to be used. At last, the program provides the parallelization command to be used for parallelizing the loop.
Multi-Oriented Text Detection and Verification in Video Frames and Scene Images
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we bring forth a novel approach of video text detection using Fourier-Laplacian filtering in the frequency domain that includes a verification technique using Hidden Markov Model (HMM). The proposed approach deals with the text region appearing not only in horizontal or vertical directions, but also in any other oblique or curved orientation in the image. Until now only a few methods have been proposed that look into curved text detection in video frames, wherein lies our novelty. In our approach, we first apply Fourier-Laplacian transform on the image followed by an ideal Laplacian-Gaussian filtering. Thereafter K-means clustering is employed to obtain the asserted text areas depending on a maximum difference map. Next, the obtained connected components (CC) are skeletonized to distinguish various text strings. Complex components are disintegrated into simpler ones according to a junction removal algorithm followed by a concatenation performed on possible combination of the disjoint skeletons to obtain the corresponding text area. Finally these text hypotheses are verified using HMM-based text/non-text classification system. False positives are thus eliminated giving us a robust text detection performance. We have tested our framework in multi-oriented text lines in four scripts, namely, English, Chinese, Devanagari and Bengali, in video frames and scene texts. The results obtained show that proposed approach surpasses existing methods on text detection.
MoodSwipe: A Soft Keyboard that Suggests Messages Based on User-Specified Emotions
Venue: arXiv cs.CL 2017
Year: 2017

We present MoodSwipe, a soft keyboard that suggests text messages given the user-specified emotions utilizing the real dialog data. The aim of MoodSwipe is to create a convenient user interface to enjoy the technology of emotion classification and text suggestion, and at the same time to collect labeled data automatically for developing more advanced technologies. While users select the MoodSwipe keyboard, they can type as usual but sense the emotion conveyed by their text and receive suggestions for their message as a benefit. In MoodSwipe, the detected emotions serve as the medium for suggested texts, where viewing the latter is the incentive to correcting the former. We conduct several experiments to show the superiority of the emotion classification models trained on the dialog data, and further to verify good emotion cues are important context for text suggestion.
Relative Rate Reduction Based Control with Adjustable Congestion Level
Venue: arXiv cs.NI 2017
Year: 2017

In Future Internet it is possible to change elements of congestion control in order to eliminate jitter and batch loss caused by the current control mechanisms based on packet loss events. We investigate the fundamental problem of adjusting sending rates to achieve optimal utilization of highly variable bandwidth of a network path using accurate packet rate information. This is done by continuously controlling the sending rate with a function of the measured packet rate at the receiver. We propose the relative loss of packet rate between the sender and the receiver (Relative Rate Reduction, RRR) as a new accurate and continuous measure of congestion of a network path, replacing the erratically fluctuating packet loss. We demonstrate that with choosing various RRR based feedback functions the optimum is reached with adjustable congestion level. The proposed method guarantees fair bandwidth sharing of competitive flows. Finally, we present testbed experiments to demonstrate the performance of the algorithm.
Language modeling with Neural trans-dimensional random fields
Venue: arXiv cs.CL 2017
Year: 2017

Trans-dimensional random field language models (TRF LMs) have recently been introduced, where sentences are modeled as a collection of random fields. The TRF approach has been shown to have the advantages of being computationally more efficient in inference than LSTM LMs with close performance and being able to flexibly integrating rich features. In this paper we propose neural TRFs, beyond of the previous discrete TRFs that only use linear potentials with discrete features. The idea is to use nonlinear potentials with continuous features, implemented by neural networks (NNs), in the TRF framework. Neural TRFs combine the advantages of both NNs and TRFs. The benefits of word embedding, nonlinear feature learning and larger context modeling are inherited from the use of NNs. At the same time, the strength of efficient inference by avoiding expensive softmax is preserved. A number of technical contributions, including employing deep convolutional neural networks (CNNs) to define the potentials and incorporating the joint stochastic approximation (JSA) strategy in the training algorithm, are developed in this work, which enable us to successfully train neural TRF LMs. Various LMs are evaluated in terms of speech recognition WERs by rescoring the 1000-best lists of WSJ'92 test data. The results show that neural TRF LMs not only improve over discrete TRF LMs, but also perform slightly better than LSTM LMs with only one fifth of parameters and 16x faster inference efficiency.
Robust Tracking and Behavioral Modeling of Movements of Biological Collectives from Ordinary Video Recordings
Venue: arXiv cs.MA 2017
Year: 2017

We propose a novel computational method to extract information about interactions among individuals with different behavioral states in a biological collective from ordinary video recordings. Assuming that individuals are acting as finite state machines, our method first detects discrete behavioral states of those individuals and then constructs a model of their state transitions, taking into account the positions and states of other individuals in the vicinity. We have tested the proposed method through applications to two real-world biological collectives: termites in an experimental setting and human pedestrians in a university campus. For each application, a robust tracking system was developed in-house, utilizing interactive human intervention (for termite tracking) or online agent-based simulation (for pedestrian tracking). In both cases, significant interactions were detected between nearby individuals with different states, demonstrating the effectiveness of the proposed method.